Small-strain shear modulus ($$G_0$$
G
0
) of soils is a crucial dynamic parameter that significantly impacts seismic site response analysis and foundation design. $$G_0$$
G
0
is susceptible to multiple factors, including soil uniformity coefficient ($$C_u$$
C
u
), void ratio (e), mean particle size ($$d_{50}$$
d
50
), and confining stress ($$\sigma '$$
σ
′
). This study aims to establish a $$G_0$$
G
0
database and suggests three advanced computational models for $$G_0$$
G
0
prediction. Nine performance indicators, including four new indices, are employed to calculate and analyze the model’s performance. The XGBoost model outperforms the other two models, with all three models achieving $$R^2$$
R
2
values exceeding 0.9, RMSE values below 30, MAE values below 25, VAF values surpassing 80%, and ARE values below 50%. Compared to the empirical formula-based traditional prediction models, the model proposed in this study exhibits better performance in IOS, IOA, a20-index, and PI metrics values. The model has higher prediction accuracy and better generalization ability.